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Proceedings Paper

Multitask SVM learning for remote sensing data classification
Author(s): Jose M. Leiva-Murillo; Luis Gómez-Chova; Gustavo Camps-Valls
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Paper Abstract

Many remote sensing data processing problems are inherently constituted by several tasks that can be solved either individually or jointly. For instance, each image in a multitemporal classification setting could be taken as an individual task but relation to previous acquisitions should be properly considered. In such problems, different modalities of the data (temporal, spatial, angular) gives rise to changes between the training and test distributions, which constitutes a difficult learning problem known as covariate shift. Multitask learning methods aim at jointly solving a set of prediction problems in an efficient way by sharing information across tasks. This paper presents a novel kernel method for multitask learning in remote sensing data classification. The proposed method alleviates the dataset shift problem by imposing cross-information in the classifiers through matrix regularization. We consider the support vector machine (SVM) as core learner and two regularization schemes are introduced: 1) the Euclidean distance of the predictors in the Hilbert space; and 2) the inclusion of relational operators between tasks. Experiments are conducted in the challenging remote sensing problems of cloud screening from multispectral MERIS images and for landmine detection.

Paper Details

Date Published: 22 October 2010
PDF: 8 pages
Proc. SPIE 7830, Image and Signal Processing for Remote Sensing XVI, 78300L (22 October 2010); doi: 10.1117/12.865045
Show Author Affiliations
Jose M. Leiva-Murillo, Univ. Carlos III de Madrid (Spain)
Luis Gómez-Chova, Univ. de València (Spain)
Gustavo Camps-Valls, Univ. de València (Spain)

Published in SPIE Proceedings Vol. 7830:
Image and Signal Processing for Remote Sensing XVI
Lorenzo Bruzzone, Editor(s)

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